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International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction

SBP 2013: Social Computing, Behavioral-Cultural Modeling and Prediction pp 424–432Cite as

Privacy Protection in Personalized Web Search: A Peer Group-Based Approach

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7812))

Abstract

Privacy protection in web search engines is becoming more and more serious in recent days. In this paper, we study the problem of privacy protection in web search, with a special focus on IP-address based personalized web search. Our goal is to break the linkage between users’ identities (e.g., IP address) and their issued queries so as to prevent privacy breaches. Our privacy model, which shares similar characteristics of l-diversity in privacy preserving data publishing of relational data, provides a strong privacy guarantee in web search. The central idea of our privacy model is to protect user’s search activities within a social peer group. A social peer group contains a set of individual users. From search engines’s perspective, search queries issued by users from the same peer group cannot be uniquely linked to individuals within the group. A framework based on grouping social peer users is proposed to achieve the privacy requirement. We also provide some experimental results to show that our methods achieve high efficiency in practice.

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References

  1. Aggarwal, G., Feder, T., Kenthapadi, K., Khuller, S., Panigrahy, R., Thomas, D., Zhu, A.: Achieving anonymity via clustering. In: Proceedings of the 25th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS 2006), pp. 153–162. ACM, New York (2006)

    Google Scholar 

  2. Blundo, C.: Private information retrieval. In: Encyclopedia of Cryptography and Security, 2nd edn., pp. 974–976 (2011)

    Google Scholar 

  3. Bornhorst, N., Pesavento, M., Gershman, A.B.: Distributed beamforming for multiuser peer-to-peer and multi-group multicasting relay networks. In: ICASSP, pp. 2800–2803 (2011)

    Google Scholar 

  4. Clark, J., van Oorschot, P.C., Adams, C.: Usability of anonymous web browsing: an examination of tor interfaces and deployability. In: Proceedings of the 3rd Symposium on Usable Privacy and Security (SOUPS 2007), pp. 41–51. ACM, New York (2007)

    Chapter  Google Scholar 

  5. Gkoulalas-Divanis, A., Verykios, V.S.: Hiding sensitive knowledge without side effects. Knowl. Inf. Syst. 20(3), 263–299 (2009)

    Article  Google Scholar 

  6. Jones, R.: Privacy in web search query log mining. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part I. LNCS (LNAI), vol. 5781, p. 4. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  7. Kim, Y., Sohn, S.Y.: Stock fraud detection using peer group analysis. Expert Syst. Appl. 39(10), 8986–8992 (2012)

    Article  Google Scholar 

  8. Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data (SIGMOD 2008), pp. 93–106. ACM Press, New York (2008)

    Chapter  Google Scholar 

  9. Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: L-diversity: Privacy beyond k-anonymity. In: Proceedings of the 22nd IEEE International Conference on Data Engineering (ICDE 2006). IEEE Computer Society, Washington, DC (2006)

    Google Scholar 

  10. Murugesan, M., Clifton, C.: Providing privacy through plausibly deniable search. In: Proceedings of the SIAM International Conference on Data Mining (SDM 2009), pp. 768–779. SIAM (2009)

    Google Scholar 

  11. Pang, H., Ding, X., Xiao, X.: Embellishing text search queries to protect user privacy. PVLDB 3(1), 598–607 (2010)

    Google Scholar 

  12. Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Transactions on Knowledge and Data Engineering (TKDE) 13(6), 1010–1027 (2001)

    Article  Google Scholar 

  13. Shtykh, R.Y., Zhang, G., Jin, Q.: Peer-to-peer solution to support group collaboration and information sharing. Int. J. Pervasive Computing and Communications 1(3), 187–198 (2005)

    Article  Google Scholar 

  14. Sweeney, L.: K-anonymity: a model for protecting privacy. International Journal on uncertainty, Fuzziness and Knowledge-based System 10(5), 557–570 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  15. Tsuneizumi, I., Aikebaier, A., Enokido, T., Takizawa, M.: A scalable peer-to-peer group communication protocol. In: AINA, pp. 268–275 (2010)

    Google Scholar 

  16. http://en.wikipedia.org/wiki/AOL_search_data_leak

  17. http://en.wikipedia.org/wiki/Proxy_server

  18. http://www.ntia.doc.gov/legacy/ntiahome/privacy/files/smith.htm

  19. Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: Proceedings of the 24th IEEE International Conference on Data Engineering (ICDE 2008), pp. 506–515. IEEE Computer Society, Cancun (2008)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Systems, University of Maryland, Baltimore County, USA

    Bin Zhou

  2. Yahoo! Labs, Beijing, China

    Jian Xu

Authors
  1. Bin Zhou
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  2. Jian Xu
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Editor information

Editors and Affiliations

  1. Applied Physics Laboratory, Research and Exploratory Development Department, Johns Hopkins University, 11100 Johns Hopkins Road, 20723, Laurel, MD, USA

    Ariel M. Greenberg  & Nathan D. Bos  & 

  2. Center for Social Complexity, Department of Computational Social Science, Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, 22030, Fairfax, VA, USA

    William G. Kennedy

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© 2013 Springer-Verlag Berlin Heidelberg

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Zhou, B., Xu, J. (2013). Privacy Protection in Personalized Web Search: A Peer Group-Based Approach. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2013. Lecture Notes in Computer Science, vol 7812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37210-0_46

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  • DOI: https://doi.org/10.1007/978-3-642-37210-0_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37209-4

  • Online ISBN: 978-3-642-37210-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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